Quantification of image clutter plays an important role in predicting target acquisition performances of a photoelectric imaging system due to the strong effect of optoelectronic image clutter. Accuracy in predicting the targeting performance of previous reported clutter metrics was relatively low because of disadvantages, such as lack of ability to accurately quantify the image with complex clutters and threshold selection problem. To address this problem, a novel multidirectionaldifference-Hash-based image clutter metric is proposed in this paper. Initially, an image similarity measure method based on multidirectional difference hash is established. Then, this method is applied to the quantification of image clutter, and an MDHash-based image clutter metric is obtained. Experimental results show that the proposed clutter metric correlates effectively with probability of detection, false alarm rate, and search time of observers.
In order to satisfy the application requirements of spaceborne three dimensional imaging lidar , a prototype of nonscanning multi-channel lidar based on receiver field of view segmentation was designed and developed. High repetition frequency micro-pulse lasers, optics fiber array and Geiger-mode APD, combination with time-correlated single photon counting technology, were adopted to achieve multi-channel detection. Ranging experiments were carried out outdoors. In low echo photon condition, target photon counting showed time correlated and noise photon counting were random. Detection probability and range precision versus threshold were described and range precision increased from 0.44 to 0.11 when threshold increased from 4 to 8.
Pulse Coupled Neural Network (PCNN) is improved by Adaptive Lateral Inhibition (ALI), while a method of infrared (IR) dim small target segmentation based on ALI-PCNN model is proposed in this paper. Firstly, the feeding input signal is modulated by lateral inhibition network to suppress background. Then, the linking input is modulated by ALI, and linking weight matrix is generated adaptively by calculating ALI coefficient of each pixel. Finally, the binary image is generated through the nonlinear modulation and the pulse generator in PCNN. The experimental results show that the segmentation effect as well as the values of contrast across region and uniformity across region of the proposed method are better than the OTSU method, maximum entropy method, the methods based on conventional PCNN and visual attention, and the proposed method has excellent performance in extracting IR dim small target from complex background.
The shapes and durations of generated pulses and the output intensity of ultrashort-pulse (USP) optical parametric generation (OPG) for two cases of λp=0.4μm and λp=0.8μm are calculated as functions of signal wavelength. The calculation result shows that the conversion efficiency of photon number reduces when the generated pulse frequencies are tuned away from the degenerate point (νs=νi=νp/2), and the signal pulse duration is lengthened when the generated pulse frequencies are tuned far away from the degenerate point. These results can be explained. For this purpose, the intrapulse group-velocity dispersion (IGVD) and group-velocity mismatch (GVM) parameters are calculated as functions of signal wavelength; a new reference of nonlinear interaction length -- pulse-leaving length is introduced to describe the effects of GVM more directly and is calculated as a function of signal wavelength; the phase mismatch for side frequencies of signal, idler and pump pulses,Δksi,Δkps and Δkpi are calculated as functions of signal wavelength.
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